Project description

Marine biogeochemical models are run as part of Earth system and climate models, to assess the contribution of marine biology to the carbon cycle, and the response of marine ecosystems to a changing climate. Physical-biogeochemical ocean models are also used to provide short-range forecasts and re-analyses of the ocean state. This provides the opportunity to use observations to constrain the models through data assimilation (DA), with satellite ocean colour providing daily global observations of the concentration of chlorophyll, an index of marine phytoplankton biomass, in the surface ocean.

The student will:

Develop a DA scheme for assimilating the outputs of satellite algorithms that split ocean colour signals into phytoplankton size classes into MEDUSA.

Implement a state-parameter estimation scheme of DA to capture simultaneous changes in the state variables and the biological parameters of MEDUSA on regional and global scales;

Incorporate in situ observations of chlorophyll, nutrients and oxygen and investigate their relationships with physical variables.

This project will develop a computationally efficient DA scheme suitable for implementation in operational forecasting or reanalysis, whilst making fullest use of the empirical information from the observations combined with knowledge of model processes. It promises an effective platform to interface the advanced satellite algorithms, and state-of-the-art ocean biogeochemical models, through DA, which may greatly improve re-analyses and forecasts of marine biogeochemistry, and contribute to our understanding of modelling marine ecosystems and the Earth's climate system.